Discrimination and Spectral Response Characteristic of Stress Leaves Infected by Rice Aphelenchoides besseyi Christie
LIU Zhan-yu1, SHI Jing-jing1, WANG Da-cheng1,2, HUANG Jing-feng1*
1. Institute of Agricultural Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100009, China
Abstract：An ASD Field Spec Pro Full Range spectrometer was used to acquire the spectral reflectance of healthy and diseased leaves infected by rice Aphelenchoides besseyi Christie, which were cut from rice individuals in the paddy field. Firstly, foliar pigment content was investigated. As compared with healthy leaves, the total chlorophyll and carotene contents (mg·g-1) of diseased leaves decreased 18% and 22%, respectively. The diseased foliar content ratio of total chlorophyll to carotene was nearly 82% of the healthy ones. Secondly, the response characteristics of hyperspectral reflectance of diseased leaves were analyzed. The spectral reflectance in the blue (450-520 nm), green (520-590 nm) and red (630-690 nm) regions were 2.5, 2 and 3.3 times the healthy ones respectively due to the decrease in foliar pigment content, whereas in the near infrared (NIR, 770-890 nm) region was 71.7 of the healthy ones because of leaf twist, and 73.7% for shortwave infrared (SWIR, 1 500-2 400 nm) region, owing to water loss. Moreover, the hyperspectral feature parameters derived from the raw spectra and the first derivative spectra were analyzed. The red edge position (REP) and blue edge position (BEP) shifted about 8 and 10 nm toward the short wavelengths respectively. The green peak position (GPP) and red trough position (RTP) shifted about 8.5 and 6 nm respectively toward the longer wavelengths. Finally, the area of the red edge peak (the sum of derivative spectra from 680 to 740 nm) and red edge position (REP) as the input vectors entered into C-SVC, which was an soft nonlinear margin classification method of support vector machine, to recognize the healthy and diseased leaves. The kernel function was radial basis function (RBF) and the value of punishment coefficient (C) was obtained from the classification model of training data sets (n=138). The performance of C-SVC was examined with the testing sample (n=126), and healthy and diseased leaves could be successfully differentiated without errors. This research demonstrated that the response feature of spectral reflectance was obvious to disease stress in rice leaves, and it was feasible to discriminate diseased leaves from healthy ones based on C-SVC model and hyperspectral reflectance.
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